YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Construction Engineering and Management
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Construction Engineering and Management
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    MOCAP and AI-Based Automated Physical Demand Analysis for Workplace Safety

    Source: Journal of Construction Engineering and Management:;2024:;Volume ( 150 ):;issue: 007::page 04024060-1
    Author:
    Ramin Aliasgari
    ,
    Chao Fan
    ,
    Xinming Li
    ,
    Ali Golabchi
    ,
    Farook Hamzeh
    DOI: 10.1061/JCEMD4.COENG-13811
    Publisher: American Society of Civil Engineers
    Abstract: Worker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace.
    • Download: (2.862Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      MOCAP and AI-Based Automated Physical Demand Analysis for Workplace Safety

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4298733
    Collections
    • Journal of Construction Engineering and Management

    Show full item record

    contributor authorRamin Aliasgari
    contributor authorChao Fan
    contributor authorXinming Li
    contributor authorAli Golabchi
    contributor authorFarook Hamzeh
    date accessioned2024-12-24T10:20:12Z
    date available2024-12-24T10:20:12Z
    date copyright7/1/2024 12:00:00 AM
    date issued2024
    identifier otherJCEMD4.COENG-13811.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298733
    description abstractWorker safety and productivity and the factors that affect them, such as ergonomics, are essential aspects of construction projects. The application of ergonomics and the identification of the connections between workers and assigned tasks have led to a decrease in worker injuries and discomfort, beneficial effects on productivity, and a reduction in project costs. Nevertheless, workers in the construction area are often subjected to awkward body postures and repetitive motions that cause musculoskeletal disorders, in turn leading to delays in production. As a systematic and widely used procedure that generates a final document or form, physical demand analysis (PDA) assesses the health and safety of workers engaged in construction or manufacturing activities and proactively evaluates ergonomic risks. However, to gather the necessary information, traditional PDA methods require ergonomists to spend significant time observing and interviewing workers. To increase the speed and accuracy of PDA, this study focuses on developing a systematic PDA framework to automatically fill a posture-based PDA form and address the physiological aspects of task demands. In contrast to the traditional observation-based approach, the proposed framework uses a motion capture (MOCAP) system and a rule-based expert system to obtain joint angles and body segment positions in different work situations, convert the measurements to objective identification of activities and their frequencies, and then automatically populate the PDA forms. The framework is tested and validated in both laboratory and on-site environments by comparing the generated forms with PDA forms filled out by ergonomists. The results indicate that the MOCAP-/AI-based automated PDA framework successfully improves the performance of PDA in terms of accuracy, consistency, and time consumption. Ultimately, this framework can aid in the design of job tasks and work environments with the goal of promoting health, safety, and productivity in the workplace.
    publisherAmerican Society of Civil Engineers
    titleMOCAP and AI-Based Automated Physical Demand Analysis for Workplace Safety
    typeJournal Article
    journal volume150
    journal issue7
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/JCEMD4.COENG-13811
    journal fristpage04024060-1
    journal lastpage04024060-23
    page23
    treeJournal of Construction Engineering and Management:;2024:;Volume ( 150 ):;issue: 007
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian